Peripheral Vascular Dysfunction Following Spinal Cord Injury: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Spinal cord injury (SCI) has been shown to impact vascular function and increase the risk of cardiovascular disease (CVD). However, data are limited regarding prognostic factors for identifying subclinical CVD risk in individuals with SCI. Objectives: To identify maladaptive structural and functional changes to central and peripheral vasculature resulting from SCI and to assess the effect of SCI on these parameters relative to able-bodied comparators. Methods: This review was prospectively registered. A systematic search was performed using PRISMA guidelines. Bias was assessed using the ROBINS-I and AHRQ tools. Studies reporting structural or functional changes to vasculature following SCI were included. Data on participant and injury characteristics, outcomes, and assessments used were extracted. Meta-analyses were conducted for adequately powered subgroups based on outcome type, measurement site, and level of injury. Results: A total of 49 studies involving 1026 individuals with SCI and 941 able-bodied comparators were included. Most studies described injury level and severity using standardized impairment classifications. Subgroup analyses showed significantly reduced arterial diameter, compliance, endothelial function, blood flow volume, and high-density lipoprotein (HDL) cholesterol concentration among people with SCI compared to controls. Intima-media thickness, arterial stiffness, shear rate, and blood glucose and triglyceride concentrations were significantly greater for people with SCI compared to controls. Additional subgroup analyses were underpowered. Conclusion: This review synthesizes the current literature reporting central and peripheral vasculature outcomes in people with SCI and able-bodied controls. Between-group differences were observed for several structural and functional vascular outcomes, which suggests that SCI has a significant impact on multiple subclinical CVD risk factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".